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Course

EE-556: Mathematics of data: from theory to computation

Summary

This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees, describe scalable solution techniques and algorithms, and illustrate the trade-offs involved.

Lectures

Instructor

Volkan Cevher

Volkan Cevher received the B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara, Turkey, in 1999 and the Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in Atlanta, GA in 2005. He was a Research Scientist with the University of Maryland, College Park from 2006-2007 and also with Rice University in Houston, TX, from 2008-2009. Currently, he is an Associate Professor at the Swiss Federal Institute of Technology Lausanne and a Faculty Fellow in the Electrical and Computer Engineering Department at Rice University. His research interests include machine learning, signal processing theory, optimization theory and methods, and information theory. Dr. Cevher is an ELLIS fellow and was the recipient of the Google Faculty Research award in 2018, the IEEE Signal Processing Society Best Paper Award in 2016, a Best Paper Award at CAMSAP in 2015, a Best Paper Award at SPARS in 2009, and an ERC CG in 2016 as well as an ERC StG in 2011.

Official source

https://edu.epfl.ch/coursebook/en/mathematics-of-data-from-theory-to-computation-EE-556

Moodle Page

http://go.epfl.ch/EE-556

Summary

Lectures

Instructor

Volkan Cevher

Official source

https://edu.epfl.ch/coursebook/en/mathematics-of-data-from-theory-to-computation-EE-556

Moodle Page

http://go.epfl.ch/EE-556

Lectures in this course (118)

Parametric Models: Mathematics of Data

Explores parametric models in data analysis, covering regression estimators, optimization problems, and statistical models.

Mathematics of Data: Overview and Examples

Covers empirical risk minimization, statistical learning, and examples of cancer prediction, house pricing, and image generation.

Optimization Basics: Linear Algebra, Analysis, Convexity

Introduces optimization basics, covering linear algebra, analysis, and convexity principles.

Convexity and Jacobians

Explores convexity, Jacobians, subdifferentials, and convergence rates in optimization and function analysis.

Deep Learning Building Blocks

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

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